{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Search Radar Output Signal to Noise Ratio\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output signal to noise ratio for a search radar may be written as (Equation 4.54)\n",
    "\n",
    "\\begin{equation}\\label{eq:radar_equation_search_final}\n",
    "    {SNR}_o = \\frac{P_{av}\\, A_e\\, \\sigma }{(4\\pi)^3\\, k\\, T_0\\, F\\, L\\, r^4} \\, \\frac{T_{scan}}{\\Omega}\n",
    "\\end{equation}\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the minimum and maximum target range (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_min_range = 10e3\n",
    "\n",
    "target_max_range = 100e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `linspace` routine from `scipy` and set up the target range array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "target_range = linspace(target_min_range, target_max_range, 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the noise figure (dB), the radar losses (dB), the target RCS (dBsm), the system temperature (K), the search volume (sr), the scan time (s), and the power aperture (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.constants import pi\n",
    "\n",
    "noise_figure = 4.0\n",
    "\n",
    "losses = 9.0\n",
    "\n",
    "target_rcs = -5.0\n",
    "\n",
    "system_temperature = 305\n",
    "\n",
    "search_volume = 2.0 * pi\n",
    "\n",
    "scan_time = 0.1\n",
    "\n",
    "power_aperture = 80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the keyword args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'target_range': target_range,\n",
    "\n",
    "           'system_temperature': system_temperature,\n",
    "\n",
    "                  'search_volume': search_volume,\n",
    "\n",
    "                  'noise_factor': 10 ** (noise_figure / 10.0),\n",
    "\n",
    "                  'losses': 10 ** (losses / 10.0),\n",
    "\n",
    "                  'power_aperture': 10 ** (power_aperture / 10.0),\n",
    "\n",
    "                  'scan_time': scan_time,\n",
    "\n",
    "                  'target_rcs': 10 ** (target_rcs / 10.0)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `output_snr` routine from `search_radar_range`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.radar_range.search_radar_range import output_snr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the output signal to noise ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    " output_snr = output_snr(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib` routines and the `log10` routine for plotting the output signal to noise ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(target_range / 1.0e3, 10.0 * log10(output_snr), '')\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Output Signal to Noise for Search', size=14)\n",
    "\n",
    "plt.xlabel('Target Range (km)', size=12)\n",
    "\n",
    "plt.ylabel('Output Signal to Noise Ratio (dB)', size=12)\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
